The present invention relates generally to image processing, and more particularly, but not by way of limitation, to a system, a method, and a recording medium including inputting a list of video images with different persons' faces and outputting a plurality of clusters, where each cluster contains the face image with the same person.
In a large media collection of people, many intelligent analyses can be made about all of the persons who are present and how they have interacted in the context of the collection. Automating these tasks is a challenge in conventional methods. Due to pose, occlusion or other artifacts (lighting, decoration, poor resolution, etc.), automating the clustering of the faces is a challenge while not knowing a priori the number of persons in the collection.
Face clustering is a task of grouping faces by visual similarity. It is closely related to face recognition, but has several different aspects. Most conventional data-driven methods are fully unsupervised, and focus on obtaining a good distance measure or mapping raw data to a new space for better representing the structure of the inter-personal dissimilarities from the unlabeled faces.
In the conventional methods of constrained clustering, many methods have been proposed to exploit pairwise constraints to guide the clustering. For example, one conventional method embeds constraints in hard manner, while other conventional methods adopt the soft constraints. However, the weights of these soft constraints are totally user-defined.
In conventional unsupervised learning tasks, it is much easier to obtain the data in “chunklets”, without the need for labels. Each chunklet is a set in which the data comes from the same class but the actual class labels are not known. Such a scenario yields partial equivalence relations. There are some conventional approaches about the learning with partial equivalence relations. One of the algorithms for this purpose is Relevant Component Analysis (RCA). RCA is an effective linear-transformed algorithm used for data representation, which finds a linear transformation of the data such that irrelevant variability in the data is reduced. This “irrelevant variability” is estimated using chunklets. A nonlinear extension of RCA called kernel RCA has been proposed in such conventional methods.
However, a major drawback of RCA, similar to Principal Component Analysis (PCA), is that the transformations of RCA are optimized for representation or compression of data in a group, but it is not good enough for class discrimination.